MBRL approaches [1]} additionally learn a world model that includes both transition function \(T\)  and reward function \(r\) . Decoupling learning dynamics of the system from the decision making allows us to use more sophisticated unsupervised learning techniques to represent current state in latent space as well as to use imaginary rollouts for training [2]}, [3]}. Many previous works provide both theoretical and empirical evidence that MBRL approaches can be much more sample efficient than their MFRL counterparts. Recent breakthroughs of Dreamer [4]}, [3]} also showed that MBRL is capable of achieving same or better performance than Model-Free approaches both in continuous and discrete environments.
